[1]} proposed a feature-based model for this task. They used lexical features such as the sequence of words between two entities and their part-of-speech (POS) tags, a flag indicating which entity appears first, \(k\)  tokens to the left of entity 1 and \(k\)  tokens to the right of entity 2, syntactic features such as dependency path between two entities, and named entity types of the two entities in their model. [2]} proposed multi-instance learning for this task to mitigate the problem of noisy sentences obtained using the distant supervision method. They used a factor graph to explicitly model the decision of whether two entities are related and whether this relation is mentioned in a given sentence. Also, they applied constraint-driven semi-supervision to train their model without any knowledge about which sentences express the relations. Their multi-instance learning model significantly improves the performance over the model proposed by [1]}.
